Monthly Streamflow Forecasting Using Convolutional Neural Network

Abstract Monthly streamflow forecasting is vital for the management of water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In the current study, the feasibility of a relatively new AI model, namely the convolutional neural network (CNN), is explored for forecasting monthly streamflow. The CNN is a method of deep learning, the unique convolution-pooling mechanism in which creates its superior attribute of automatically extracting critical features from input layers. Hydrological and large-scale atmospheric circulation variables including rainfall, streamflow, and atmospheric circulation factors (ACFs) are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The ANN and ELM with inputs identified based on cross-correlation analysis (CC) and mutual information analysis (MI) are established for comparative analysis. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN performs better than ANN and ELM across all the statistical measures. Moreover, CNN shows better stability in forecasting accuracy.

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Remote-Sensing-Based Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models

Remote Sensing ◽

10.3390/rs13204147 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4147

Author(s):

Mohammed M. Alquraish ◽

Mosaad Khadr

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Superior Performance ◽

Support Vector ◽

Streamflow Forecasting ◽

Forecasting Models ◽

Monthly Streamflow ◽

Artificial Neural ◽

Monthly Streamflow Forecasting

In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution data derived from these products. The hydrological performance of the developed forecasting models was then evaluated using several statistical indices, including NSE, MAE, RMSE, and R2. The performance measures confirmed that the CHIRPS product has superior performance compared to RFE 2.0 and TRMM data, and it could provide reliable rainfall estimates for use as input in forecasting models. Likewise, the results of the forecasting models confirmed that the ANN and SVM both achieved acceptable levels of accuracy for forecasting streamflow; however, the ANN model was superior (R2 = 0.898–0.735) to the SVM (R2 = 0.742–0.635) in both the training and testing periods.

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A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Water Resources Management ◽

10.1007/s11269-017-1807-2 ◽

2017 ◽

Vol 32 (1) ◽

pp. 243-258 ◽

Cited By ~ 19

Author(s):

Fereshteh Modaresi ◽

Shahab Araghinejad ◽

Kumars Ebrahimi

Keyword(s):

Neural Network ◽

Nearest Neighbor ◽

Comparative Assessment ◽

Least Square ◽

Support Vector ◽

Generalized Regression Neural Network ◽

Streamflow Forecasting ◽

K Nearest Neighbor ◽

Monthly Streamflow ◽

Monthly Streamflow Forecasting

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A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

Journal of Hydrology ◽

10.1016/j.jhydrol.2016.06.026 ◽

2016 ◽

Vol 540 ◽

pp. 623-640 ◽

Cited By ~ 72

Author(s):

Greer B. Humphrey ◽

Matthew S. Gibbs ◽

Graeme C. Dandy ◽

Holger R. Maier

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hydrological Model ◽

Hybrid Approach ◽

Streamflow Forecasting ◽

Monthly Streamflow ◽

Artificial Neural ◽

Monthly Streamflow Forecasting

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Selected model fusion: an approach for improving the accuracy of monthly streamflow forecasting

Journal of Hydroinformatics ◽

10.2166/hydro.2018.098 ◽

2018 ◽

Vol 20 (4) ◽

pp. 917-933 ◽

Cited By ~ 3

Author(s):

Fereshteh Modaresi ◽

Shahab Araghinejad ◽

Kumars Ebrahimi

Keyword(s):

Neural Network ◽

Nearest Neighbor ◽

Least Square ◽

Support Vector ◽

Generalized Regression Neural Network ◽

Streamflow Forecasting ◽

K Nearest Neighbor ◽

Model Fusion ◽

Monthly Streamflow ◽

Monthly Streamflow Forecasting

Abstract Monthly streamflow forecasting plays an important role in water resources management, especially for dam operation. In this paper, an approach of model fusion technique named selected model fusion (SMF) is applied and assessed under two strategies of model selection in order to improve the accuracy of streamflow forecasting. The two strategies of SMF are: fusion of the outputs of best individual forecasting models (IFMs) selected by dendrogram analysis (S1), and fusion of the best outputs of all IFMs resulting from an ordered selection algorithm (S2). In both strategies, five data-driven models including: artificial neural network, generalized regression neural network, least square-support vector regression, K-nearest neighbor regression, and multiple linear regression with optimized structure are performed as IFMs. The SMF strategies are applied for forecasting the monthly inflow to Karkheh reservoir, Iran, owning various patterns between predictor and predicted variables in different months. Results show that applying SMF approach based on both strategies results in more accurate forecasts in comparison with fusion of all IFMs outputs (S3), as the benchmark. However, comparison of the two SMF strategies reveals that the implementation of strategy (S2) considerably improves the accuracy of forecasts than strategy (S1) as well as the best IFM results (S4) in all months.

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